Presentation Blocks: 03-23-2018 - Friday - 11:00 AM - 12:15 PM

Title: Identification of Bacteria in Saliva by Surface Enhanced Raman Spectroscopy


Luis Martinez (Presenter)

John Simecek, NAMRU- SA


Objectives: Surface enhanced Raman spectroscopy (SERS) shows promise for rapidly diagnosing bacterial pathogens involved in oral disease.
The objective of this research is to evaluate the DXR™ Raman microscope to identify bacteria of dental interest in different matrices.

Methods: Staphylococcus aureus, Streptococcus mutans, and an equal 1:1 ratio mixture of the two at concentrations ranging from 106 to 108 colony forming units were re-suspended in water or saliva. From the saliva sample, 900 µl was processed with a lysis buffer and re-suspended in water and 100 µl was left in saliva. All samples were scanned using the 10X objective of the Raman microscope to determine signature peaks. MATLAB software was used to build principal component analysis (PCA) models for bacterial samples in (1) water, (2) saliva, (3) processed saliva, and (4) mixtures of the two bacteria in water, processed, and unprocessed saliva.

Results: A unique SERS peak at 1087 cm-1 was identified for S. mutans in the water and saliva samples, however, the peak was not observed in the processed saliva. Principal component analysis (PCA) plotting showed distinct differentiation of the bacteria with the first three principal components accounting for 96% of the variance in the spectra. PCA identified S. mutans and S. aureus in water and saliva; however, processed samples of S. mutans were identified as S. aureus, possibly due to the removal of the peak at 1087 cm-1. Mixed population samples in processed saliva and water were identified as S. aureus, while the mixed population in unprocessed saliva was identified as S. mutans.

Conclusions: SERS showed the capability to differentiate two individual bacterial species in water or saliva matrices, but further analysis is required to identify mixed populations. Sample processing is not advocated, as it may affect correct identification. SERS technology is a potential point-of-care diagnostic platform.